Informatics and Applications
2020, Volume 14, Issue 2, pp 86-91
SEQUENTIAL ANALYSIS OF SERIAL MEASUREMENTS BASED ON MULTIVARIATE REFERENCE REGIONS
Abstract
Sequential data series analysis procedures are considered. An approach is developed when a set of
multivariate features of a certain object, which varies in time, is presented as a single vector of observed values.
By increasing the dimensionality of the data, it is possible to obtain a single picture of the description of objects,
to take into account the objectively existing dependence of individual observations, and to simulate changes over
time. The basis for solving classification problems is the use of multivariate reference regions. Three options
for data processing procedures are proposed, their properties are investigated, and recommendations for practical
application are developed. To demonstrate the capabilities of these procedures, the task of early diagnosis of cancer
using the PSA (prostate-specific antigen) biomarker is considered. Features of the application of sequential methods
for analyzing data series are indicated, recommendations for their effective use are formed, and the advantages of
the consolidating approach in data analysis are identified.
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[+] About this article
Title
SEQUENTIAL ANALYSIS OF SERIAL MEASUREMENTS BASED ON MULTIVARIATE REFERENCE REGIONS
Journal
Informatics and Applications
2020, Volume 14, Issue 2, pp 86-91
Cover Date
2020-06-30
DOI
10.14357/19922264200212
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
serial measurements; consolidation approach; sequential procedures; analysis of prostate-specific antigen (PSA)
Authors
M. P. Krivenko 
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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